Journal
APPLIED SCIENCES-BASEL
Volume 8, Issue 11, Pages -Publisher
MDPI
DOI: 10.3390/app8112078
Keywords
Lithium-ion battery; remaining useful life; health indictor; long short-term memory
Categories
Funding
- Nature Science Foundation of China [61873122, 61673206]
- Equipment Pre-research National Defense Science Technology Key Laboratory Foundation [61422080307]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX18_0300]
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On-line remaining-useful-life (RUL) prognosis is still a problem for satellite Lithium-ion (Li-ion) batteries. Meanwhile, capacity, widely used as a health indicator of a battery (HI), is inconvenient or even impossible to measure. Aiming at practical and precise prediction of the RUL of satellite Li-ion batteries, a dynamic long short-term memory (DLSTM) neural-network-based indirect RUL prognosis is proposed in this paper. Firstly, an indirect HI based on the Spearman correlation analysis method is extracted from the battery discharge voltages, and the relationship between the indirect HI indices and battery capacity is established using a polynomial fitting method. Then, by integrating the Adam method, L2 regularization method, and incremental learning, a DLSTM method is proposed and applied for Li-ion battery RUL prognosis. Finally, verification of the results on NASA #5 battery data sets demonstrates that the proposed method has better dynamic performance and higher accuracy than the three other popular methods.
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